In communications networks, there may be a challenge to obtain good performance and capacity for a given communications protocol, its parameters and the physical environment in which the communications network is deployed.
For example, transmit (Tx) beamforming, or precoding, is a mechanism that can improve performance in wireless networks by focusing the transmitted energy in space. Beamforming can be implemented with array antennas using analog or digital beamforming or a combination thereof. In line-of-sight (LoS) channels the signals on different antenna elements will be correlated. Ideally, assuming a single planar wavefront and a narrowband signal, M antenna elements can be phase aligned to coherently combine the signal in a desired direction giving a beamforming gain of a factor of M. This corresponds to a pencil beam pointing in the desired direction.
In many channels there will not be any LoS component. The information transfer then relies upon multipath propagation. Generally, multipath propagation will decorrelate the signals on different antenna elements which will reduce the beamforming gain if conventional beamforming is used. However, if channel state information (CSI) is available at the transmitter, multipath propagation paths can be combined coherently by proper amplitude and phase weighting on each antenna element. In this way, full beamforming gain can be achieved also in multipath propagation conditions. More precisely, the beamforming weights that can achieve this are given byw=chH  (1)where w is the unit-norm beamforming weight vector, c is a scalar normalization constant, h is the channel response vector between the antenna array at the radio access network node and the antenna at the wireless device served by the radio access network node, and H denotes complex conjugate transpose. It is here assumed that the wireless device comprises a single antenna element for communicating with the radio access network node. This particular choice of beamforming weights is often referred to as maximum ratio transmission (MRT) or matched filter (MF) precoding.
In a pure LoS channel with a single planar wavefront and assuming a uniform linear array (ULA) at the radio access network node, the m:th element in the channel vector is given byhm=exp(−j2πmd sin ϕ)where d is the antenna element spacing measured in wavelengths and ϕ is the angle-of-departure (AoD) to the user equipment (UE). Hence, in sin ϕ space, the channel vector is simply a discrete Fourier transform (DFT) vector. Beamforming using DFT weight vectors will be referred to as DFT beamforming.
Massive beamforming is foreseen as a candidate component for the next generation cellular communications system, denoted 5G. In general terms, massive beamforming implies that the radio access network node is equipped with antenna arrays with a large number of antenna elements, orders of magnitudes larger than used in current radio access network nodes. This is expected to mitigate the increased propagation loss when operating in the new, higher frequency bands that are considered for the proposed so-called 5G telecommunications standard. This mitigation is foreseen to be achieved by the high beamforming gain that could be obtained with large antenna arrays. Generally, the high beamforming gain may be achieved by DFT beamforming if there is LoS or MRT if there is no LoS.
If Tx/receive (Rx) reciprocity cannot be assumed, the CSI needs to be obtained by feedback from the wireless device. Reciprocity cannot typically be assumed for frequency-division duplex (FDD) systems due to the frequency difference between the Tx and Rx bands. Another cause of non-reciprocity is that the Tx and Rx branches may not have the same characteristics.
The CSI needed for DFT beamforming relates to the AoD to the wireless device. This can be estimated by enabling the radio access network node transmit reference signals in a number of hypothesized AoDs and enabling the wireless device to report the estimated direction that gives the maximum received power for the wireless device. This may be performed by having a fixed grid-of-beams (GoB) at the radio access network node and associate each beam in the grid with a unique reference signal sequence. Each beam in the grid may be a DFT beam and the beam grid may span the angular coverage of the cell defined by the region in which the radio access network node provides service. The wireless device is then configured to measure the reference signal received power (RSRP) of each beam and reports this (for a number of beams or only the best beam, i.e., the beam with highest RSRP) back to the radio access network node.
An alternative approach to obtain the CSI is that the wireless device instead has access to information regarding which beams that are available in the GoB; this set of beams is commonly referred to as a codebook. By the radio access network node transmitting pilot sequences on each antenna port, the wireless device will be able to estimate the entire channel h and based on this the wireless device will further be able to recommend one, or multiple, of the beams within the GoB. This information can then be fed back from the wireless device to the radio access network node.
For MRT full channel knowledge, i.e., the amplitude and phase of each channel coefficient, is needed at the radio access network node. For a non-reciprocal system this implies that the wireless device needs to report amplitude and phase of each channel coefficient back to the radio access network node.
However, there are still some issues with massive beamforming, particularly for massive beamforming used in non-LoS (NLoS) channels. As a first example, DFT beamforming as described above does not yield full beamforming gain in NLoS channels. If the channel angular spread at the radio access network node is larger than the beamwidth of the DFT beams in the GoB, significant loss in beamforming gain will occur. With large arrays the beamwidth is very small which means that even a small angular spread will lead to loss in beamforming gain. As a second example, MRT may achieve full beamforming gain regardless of the channel angular spread, but the feedback overhead resulting from the needed CSI is prohibitive for large arrays.
Hence, there is still a need for improved precoding for massive beamforming.